For years, when I talked to people about AI, I could see a gap between what they expected and what they got. They expected something that would just do the job. Tell it what you want, it goes away, and the work comes back done. What they actually got was a chat box. Clever, sometimes impressive, but you still did all the running around yourself. Copy this, paste that, open another app, reformat it, send it.
Over the last little while I’ve been using Grok Bot heavily across my own business, and for the first time it feels like that gap is closing. Not because the AI suddenly got smarter, although it has, but because it’s easy to set up so that it actually gets things done. That’s the shift I want to talk about, because I think it’s why it’s fast becoming a real option for a lot of businesses, and particularly for SMBs and the MSPs who look after them.
It works the way people always expected AI to work
Most people never cared which model they were using. They cared about outcomes. Did the email get answered? Did the report get written? Did the post go up on time? The industry spent a long time arguing about models and benchmarks while the average business owner just wanted someone, or something, to take a task off their plate.
What I notice with Grok Bot is that I rarely think about the model at all. I think about the job. I describe what I want in plain language, and the assistant works out the steps. It can reach the systems I’ve connected, use a computer and browser when there isn’t a connector, and come back to me with a result rather than a suggestion. That’s much closer to the mental picture most people have always had of an assistant.
I touched on this idea in a recent podcast, Need to Know podcast – Episode 372, where I talked about where AI is heading. The theme is the same: the model is becoming the engine, and what really matters is what it’s connected to and what it’s allowed to do.
What I actually use it for
Rather than talk in the abstract, here’s how I use it day to day. I don’t run one general assistant. I run a number of specialised ones, each set up for a particular job.
- Blog writing and scheduling. One assistant drafts articles in my voice, creates a hero image, uploads it to WordPress, picks tags and schedules the post for the next free morning slot. The article you’re reading started there, and it waited as a draft until I approved it.
- Social posting. Separate assistants turn blog posts into LinkedIn and X posts, so new content gets shared without me rewriting it each time.
- Daily Microsoft 365 security checks. An assistant runs regular checks across my tenants and reports what changed or what looks wrong, so I start the day knowing where to look.
- News briefs. Another pulls together the Microsoft and security news I care about into a regular brief.
- Podcast preparation. One helps me research news and build outlines and scripts for episodes.
- Teams, help desk and email list. Others watch and respond in Teams, help triage support questions and help look after my email list.
The common thread is that each assistant knows its job, knows my standards and knows which tools it can use. When I want to change how something works, I tell it in plain language and it updates how it goes about the task next time. There’s no project plan or developer involved.
None of these needed me to write code. Each one is mostly a set of plain-language instructions, a few connected services and, where it makes sense, a schedule.
Connectors are where the work happens
The thing that turns an AI from a talker into a doer is connections. In my case that means WordPress, Outlook, Teams, GitHub and others. Once an assistant can reach those, a request like “draft this, put it on the blog and schedule it” isn’t a lesson in copy and paste anymore. It’s a task that gets finished.
When there isn’t a connector, there’s a shared computer with a browser the assistants can use. That matters more than it sounds. Plenty of the systems SMBs rely on don’t have neat integrations. Being able to fall back to using a website the way a person would fills a lot of gaps.
Just as importantly, it asks before doing anything that can’t easily be undone or that other people will see. Sending messages, posting publicly, deleting things. That’s the right balance. I want it to do the legwork, but I still want to approve what goes out under my name.
Routines mean it works while I don’t
The other big piece is routines. I can set an assistant to run on a schedule, every morning, every week, or when something happens. My security checks and news briefs run without me asking. When I sit down, the work is already done and waiting for review.
This is the part I think many businesses will find most valuable. A lot of small business admin isn’t hard. It’s just relentless. Checking things, summarising things, chasing things. Handing that rhythm to an assistant frees up real time.
See it in action
If you’re an MSP wondering what this looks like in practice, I’ve recorded a video that shows it. In How I Use AI to Automate Microsoft 365 Administration, Security & Reporting I walk through how I’m using AI to handle the administration, security and reporting work across Microsoft 365 that used to eat my time. It’s the recording of my October Need to Know webinar on managing Microsoft 365 autonomously, so it’s practical rather than theoretical.
Watch it with your own customers in mind. Think about which of their routine checks and reports you could hand off in the same way.
Why this matters for SMBs and MSPs
Picture a ten-person business with no IT staff. They don’t want to evaluate models or build integrations. They want their quotes followed up, their inbox summarised and their social media kept alive. An assistant they can configure in plain English, connect to the tools they already use and put on a schedule is a very different proposition from a chatbot they have to babysit.
For MSPs, I see two opportunities. The first is internal. Your own team can hand off routine checks, reporting and documentation. The second is with customers. Someone still needs to design these assistants well, decide what they can touch, secure the connections and review the results. That’s advisory work, and it’s exactly where a good MSP adds value.
It also raises the same questions I’d ask of any AI tool. What data can it reach? Who approves the risky steps? How do you know what it did? Easy to configure doesn’t mean you skip the thinking. It means the thinking goes into the design rather than the plumbing.
A few lessons from my own setup
- Specialise. Several focused assistants work better than one that tries to do everything.
- Write down your standards. The more clearly I describe my voice, formatting and rules, the more consistent the results.
- Start with drafts. Let it prepare, and you approve, until you trust it with more.
- Automate the rhythm, not just the task. The real gains come when work runs on a schedule.
Getting things done is the point
I don’t think the next phase of AI for business will be won by whoever has the best model on a leaderboard. It will be won by whatever makes it easiest for ordinary businesses to get real work done, reliably and safely.
That’s why I’ve leaned into Grok Bot. Not because of what’s under the bonnet, but because it does what I always expected AI to do. I tell it what I need, and it gets on with it. For a lot of businesses, that’s the only measure that has ever really mattered.
